1. Field of the Invention
The present invention relates to process control where at least some of the controllable parameters are difficult or impossible to characterize or are unknown. More particularly, the present invention relates to process control in biotechnology. Even more particularly, the present invention relates to process control in biotechnology minerals processing. In particular, an application of the present invention relates to a Bioexpert, learning-based controller for bioprocess alteration of minerals for hydrometallurgical processing.
2. Relevant Technology
Traditional process control technology uses mathematical models based on well-defined and well-measured process states. Thereby, allowing the use of mathematical and well-understood control schemes or a series of control schemes, such as PID, Pole Placement, LQR/LTG, H∞, ARMA based Adaptive Control, etc., to control the processes.
The biotechnology processing industry differs greatly from the more traditional process control industry. This difference largely stems from the biological uncertainties and complexities of these systems. These uncertainties manifest themselves in a radically more complicated process mechanisms and interactions to the process operator through the process"" varying, undefinable and unpredictable nature. To this end, process control engineers in the biotech industry rely upon well-defined environmental process disturbances such as temperature, pH, electrode potential, dissolved oxygen (DO), biomass density, and process flow rates to control the process. The bioprocess engineer acts to restrictively minimize the effects of environmental disturbances upon the process due to the highly complex nature of a biosystem""s reaction to these environmental disturbances.
In the mining industry, it is well known that an ore body will have both gradual and radical changes in its composition throughout the ore body. As the ore is mined by the mining engineer, the process engineer must work closely with the mining engineer and the laboratory in characterizing the chemical and physical nature of the ore body. This newly developed characterization must be taken into account when the ore body is processed within the mill in order to continue the maximum production rates. Traditionally, this could involve recalculation of the process control parameters.
Additionally, a process engineer in the mining industry has the problem of transferring experience learned during the processing of one ore body to subsequent ore bodies that are to be processed. This problem is due to the fact that ore bodies are usually highly site specific, i.e. composition specific, such that an entirely different process stream, processing approach, and process control scheme must be taken.
When a process engineer integrates bioprocessing with minerals processing, the complexity of the combination often becomes greater than the sum of its parts. Particularly, a culture of minerals-processing-microorganisms designed for the ore refining/leaching process will respond in complicated and unpredictable ways to well-defined process disturbances such as temperature, pH, flow rate, and the like. It is these very facts that limits the use of mathematical model based control schemes. Added to these complications of the bioprocessing field are the site-specific nature of each ore body and the sometimes radical variations of the chemical and physical qualities of the ore within a single ore body.
Microbial treatment has been proven to be an economically viable approach for the recovery of metals from some low-grade ores. Minerals bioprocessing utilizes mixed cultures of iron- and sulfur-oxidizing acidophilic bacteria that cause the oxidation of mineral structures with the concomitant liberation of metals from the ore. During biological ore oxidation, the microbial population change, the pH of the environment can increase or decrease, temperature generally increases, dissolved O2 and CO2 concentrations decrease, and the concentration of metals in the leach liquor generally increases.
Due to the elevated temperatures (50 to 60xc2x0 C. and higher) that can be achieved during biological heap-leaching operations, moderately thermophilic bacteria are being considered as a way to extend the operating temperature range and improve oxidation efficiency in the heaps. Moderately thermophilic bacteria have been isolated from acidic coal dumps, ore deposits, mining operations, and hot spring environments. They vary in their ability to oxidize iron, sulfur, and pyrite as well as in their ability to grow autotrophically or heterotrophically. Temperature, pH, metal concentration, O2, CO2, and pulp density are known to affect growth and mineral oxidation by acidophilic bacteria. However, in a minerals processing environment in which any number of physical and chemical parameters are changing, the extent to which these parameters interact and impact iron oxidation by moderately thermophilic bacteria is unknown.
Moreover, minerals bioprocessing comprises very complex systems. The physical, chemical and biological components for minerals bioprocessing are not well characterized. In addition, the physical, chemical and microbial populations that are most favorable for optimum yields are often unknown and are radically different from one mine site to the next. More importantly, the physical and chemical conditions and microbial populations associated with these bioprocesses change with time or, as evident in heap leaching operations, are spatially variable. The changing conditions and microbial populations make it vital that a control system be robust and able to adapt to these changes. These underlining features rule out the use of traditional knowledge-based, neural network, fuzzy logic, and model-based intelligent controllers. Furthermore, traditional process optimization procedures may be inadequate to control highly variable, uncharacterized or unknowable conditions in minerals bioprocessing.
The minerals processing engineer using a bioprocess must stand a round-the-clock watching of all process parameters in an attempt to optimize minerals recovery while providing for the needs of the microorganism culture using his heuristic process knowledge. The minerals processing engineer routinely seeks correlation between input and output in order to simplify processing decisions and to maximize recovery. Many times the possible correlations in a microorganism minerals processing system become too many, too varied, and too complex. This makes the task of tracking any possible correlations between process inputs and goals an accounting nightmare, let alone a nearly unfeasible task. Additionally, because biotechnology processes use microorganisms, the microorganism nursery itself can become the source of a processing problem wherein contamination or inoculation of a culture from other microorganisms can kill, render ineffective, or even cause an optimizing transmutation of the microorganisms that will affect the biotechnology process in question.
What is needed is a method of controlling a process that can deal with the complexity of a bio-minerals process and that adjusts to an uncharacterized and unknown set of environmental changes.
What is also needed in the art is a method of cultivating microorganisms for biotechnology minerals processing that overcomes the problems of the prior art. What is also needed in the art is a method of minerals processing by contacting and maintaining a microbial population within an ore body. Additionally, mixed culture bioprocesses, such as those found in the mining industry, need to be developed and evaluated under conditions that will address more accurately the challenges of involved in this field. Intelligent control technologies need to be designed to handle the complexities inherent when examining multi-parametric effects on growth and metabolism by bacteria or when developing control strategies that are approximate for minerals processing bioprocesses.
An inventive stochastic reinforcement, learning-based control system was developed and applied to the supervision of uncharacterized, moderately thermophilic bacterial culture in a continuous stirred tank reactor (CSTR). The inventive system had as a process goal, e.g. to optimize the production of oxidized iron.
The control system has the ability to select environmental set point conditions, maintain those set points, analyze system states, and to recognize and diagnose instrument faults for the operator. Through the use of a stochastic reinforcement learning algorithm, the control system has the ability to adapt and optimize the uncharacterized process, such as the iron oxidation process performed by the thermophilic bacteria, within a mathematical process model. The inventive system serves as an outer loop controller, deriving its information from a plurality of inner loop controllers, sensor packages and a diagnostic system. The inventive system issues set point control values, e.g. pH, dilution rate, and temperature, to both traditional and intelligent inner loop controllers. Moreover, these inner loop controllers also function as tiered systems. That is the first layer of inner loop controllers in turn drive second layer inner loop fuzzy-based pH and temperature controllers, which also drives third layer inner loop fuzzy-based pump controllers.
The inventive method may include a software module that is one component of an hierarchical hardware and software system developed for the intelligent control of iron oxidation by or the cultivation of minerals processing microorganisms in a CSTR. The control system may use on-line sensors and off-line measurements to determine the state of the system and the state of the process goals. The inventive method is used to determine what actions are required to maximize process goals. These actions are carried out by lower level controllers using computer-controlled actuators, such as pumps, gas-flow valves, heaters, and stirrers.
The inventive method may use stochastic learning to determine what the system environmental/input parameters should be, based upon the current state and past history of the system. Lower-level fuzzy systems and standard classical methods within the lower lever controllers may then be used to actuate those commands and perturb the system to achieve the desired goals. The diagnostic system analyzes the sensor data for inconsistencies and provides a log of the system operation.
Identification of the state of the system precedes the decisions of how the directly controllable parameters should be changed. These decisions may be based upon the control strategy and are carried out according to the inventive method. Two such methods are described herein. The first procedure for process operation according to the inventive method may be carried out as follows:
1. Choose a new set of process parameters.
2. Run the system to steady state at a given set of conditions.
3. Determine process output parameter values.
4. Has the goal or goals been maximized for the current choice of set points?
If yes, then maintain the process (until a change occurs), go to 2.
If no, then return to 1.
The second procedure for process operation according to the inventive method may be carried out as follows:
1. Choose a set of process parameters.
2. Run the system to steady state at a given set of conditions.
3. Determine process output parameter values.
4. Change at least one process/environmental input parameter according to the inventive method.
5. Run the system to steady state under the at least one changed input parameter.
6. Determine process output parameter values.
7. Has the goal or goals been maximized for the current choice of set points?
If yes, then choose a new set of process input parameters, different from those last chosen and go to 2.
If no, then return to 4.
Working examples for both methods are described within the preferred embodiments. In specific the second method, mainly step 4, used a working example based on flow rate, an input parameter and peak production, an output parameter. The flow rate was chosen based on the approximate range of values that should bracket the peak production value using an expert system and an optimization algorithm. The initial flow rate was chosen by the operator, alternatively the inventive system controller was set to the last best value found or a default value that is in the center of the range. The second value was chosen to be 50% higher than the first value. The third value was chosen to be about 50% higher than the first value if the production rate increased. Alternatively, the third value was chosen to be about 33% lower than the first value if the production rate decreased.
After three or more different flow rate settings the optimization algorithm, a second-order polynomial fit was made to the data and the flow rate for maximum productivity was found from this function. The process terminated when the flow rate for the estimated maximum productivity was within the specified tolerance of the last trial.
In order to choose a new set of process parameters, a stochastic learning scheme was used to determine the temperature, pH, and iron inlet concentration. The temperature, pH, and iron inlet concentration were each given an inventive initial two-sided Gaussian distribution with defined limits. The low pH limit was based on what was expected to be the lowest pH at which growth would occur at a rate that could be accommodated without going to extremely low flow rates (outside of the low level controllers abilities) and, moreover, extremely long testing periods. The higher pH limit was the highest value that would not result in the precipitation of iron-hydroxides. The temperature range was based on the result of an experiment which examined the effects of temperature on biomass growth and iron oxidation. The lower inlet iron concentration was based on the lower flow limits of the flow equipment. The higher inlet iron concentration was based on the concentration that was used in the enrichment of a moderately thermophilic culture from a mining operation.
The center for these distributions were used as the initial set points. The initial widths of the distributions were chosen to span reasonable operating values for each parameter. When a new set point (pH, temperature,inlet iron concentration) was required as in Step 7 of the control strategy, a random number generator based on the above inventive distributions was used to select the new set points. The stochastic learning took place by adjusting the inventive distributions, mean, and composition standard deviation, depending on the relative production rates. If the rate improved with the new values, the center of the distribution was shifted to the new value and the width was increased in the direction of the change, while the width in the opposite direction was reduced. However, if the rate did not improve, the center of the distribution did not change, and the width in the direction of the change was decreased, as before the opposite direction was increased. The steps of the inventive algorithm may be illustrated in code as follows:
When at least one of the currently monitored process goals is better than the best recorded of the same, at least one of the process goals, then
When the best set point is less than the current set point then
increase the positive distribution width by a prescribed function which is based on at least the current set point and the best set point, decrease the negative width by the function or another function, reset the best set point to the current set point, and move the distribution to reflect the new best set point
Otherwise
decrease the positive distribution width by a prescribed function which is based on the current set point and the best set point, likewise increase the negative width by said function or another function, reset the best set point to the current set point, and move the distribution to reflect the new best set point
Otherwise
When the best set point is less than the current set point then
decrease the positive distribution width by a prescribed function which is based on the current set point and the best set point, increase the negative width by the function or another function
Otherwise
increase the positive distribution width by a prescribed function which is based on the current set point and the best set point, decrease the negative width by the function or another function
Loop.
The combined effects of pH, temperature, and iron concentration on growth and iron oxidation by moderately thermophilic, acidophilic enrichment cultures were examined in a continuous culture. The inventive control system was used to acquire and analyze the data then to select and maintain the sets of conditions that were evaluated. Originally, the cultures had been derived from a heap-leaching operation by cultivation at 55xc2x0 C. in an acidic medium (pH 1.8) containing yeast extract and iron (100 mM Fe2+). Data indicated that pH was important, but not the only parameter that affected iron oxidation and growth.
A relatively high pH in combination with a relatively low temperature, or a relatively low pH in combination with a relatively high temperature resulted in moderate to high oxidation rates. However, the cultures appeared sensitive to the combined effects of a relatively high pH (pH 1.84) and high temperature (51.5xc2x0 C.). Under these conditions little cell growth and iron oxidation were observed. In a mixed culture containing mesophilic and thermophilic bacteria, the computer learned that at pH 1.8, 45xc2x0 C., and an inlet iron concentration of 30-35 mM were most favorable for iron oxidation of the ore samples that were tested.
The results disclosed herein demonstrate an interactive effect between pH and temperature that impacted growth and iron oxidation by moderately thermophilic bacteria. In addition, the present invention demonstrates the use of an intelligent control system as a tool that can be used to understand the interactive effects of environmental parameters on microbial activity.
In the control of biological processes, the present invention demonstrates that intelligent sensing and control technologies can be used in situations in which conventional set point control strategies are not adequate or are impossible due to the lack of mathematical models required to implement them. The inventive system can handle uncertainty, qualitative knowledge, poorly or incompletely modeled processes, and unexpected events. In short, the inventive system is more robust than traditional control strategies with these conditions. The inventive system monitors the process itself and the product, as well as the process parameters, and can control the final product quality using mappings based on analytical models, numerical models, engineering experience, and qualitative operational knowledge. In short, the inventive system has been created to replace the human operator/engineer/scientist when optimization is required for a uncharacterized system or process.
Accordingly, it is an object of the present invention to control a system by an algorithm, wherein the system has intrinsic uncertainty, poorly or incompletely modeled process parameters, unpredictably changing process extrema (i.e. maxima and minima), unexpected load change events, and the like.
It is also an object of the invention to provide a process control system that optimizes processing in the face of undefined or poorly defined process parameters. It is also an object of the invention to provide a system that seeks a local optimum or local optima in a process in which elements of the process change in difficult-to-characterize ways. It is also an object of the invention to provide a process that learns from processing data and chooses weighted variable values between processing elements and products.
It is also an object of the present invention to provide a process control system that uses biological components that are uncharacterized at any given time and that are manipulated for process optimization without correlated or rigorous models thereof.
It is also an object of the present invention to provide a system that combines the inventive method with minerals processing that uses microorganisms that overcomes the problems of the prior art.
It is also an object of the present invention to provide a process control system that uses mineralogical components that are uncharacterized at any given time and that are manipulated for process optimization without correlated or rigorous models thereof.
It is also an object of the present invention to provide a process control system that uses a mixture of biological and mineralogical components, either or both of which are uncharacterized at any given time, and that are manipulated for process optimization without correlated or rigorous models thereof.
These and other objects, features, and advantages of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth hereinafter.